Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 328935, 19 pages http://dx.doi.org/10.1155/2014/328935

Research Article miRNA Transcriptome of Hypertrophic Skeletal Muscle with Overexpressed Myostatin Propeptide Ruheena Javed,1 Lu Jing,1 Jinzeng Yang,2 Xinyun Li,1 Jianhua Cao,1 and Shuhong Zhao1 1

Department of Animal Genetics & Breeding, College of Animal Science and Veterinary Medicine, Huazhong Agricultural University, Wuhan, Hubei Province 430070, China 2 Department of Human Nutrition, Food and Animal Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA Correspondence should be addressed to Shuhong Zhao; [email protected] Received 21 April 2014; Revised 9 June 2014; Accepted 16 June 2014; Published 24 July 2014 Academic Editor: Qinghua Nie Copyright © 2014 Ruheena Javed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MicroRNAs (miRNAs) play an imperative role in cell proliferation, differentiation, and cell metabolism through regulation of gene expression. Skeletal muscle hypertrophy that results from myostatin depression by its propeptide provides an interesting model to understand how miRNA transcriptome is involved in myostatin-based fiber hypertrophy. This study employed Solexa deep sequencing followed by Q-PCR methods to analyze miRNA transcriptome of skeletal muscle of myostatin propeptide transgenic mice in comparison with their littermate controls. A total of 461 mature known and 69 novel miRNAs were reported from this study. Fifty-seven miRNAs were expressed differentially between transgenic and littermate controls, of which most abundant miRNAs, miR-133a and 378a, were significantly differentially expressed. Expression profiling was validated on 8 known and 2 novel miRNAs. The miRNA targets prediction and pathway analysis showed that FST, SMAD3, TGFBR1, and AcvR1a genes play a vital role in skeletal muscle hypertrophy in the myostatin propeptide transgenic mice. It is predicted that miR-101 targeted to TGFBR1 and SMAD3, miR-425 to TGFBR2 and FST, and miR-199a to AcvR2a and TGF-𝛽 genes. In conclusion, the study offers initial miRNA profiling and methodology of miRNA targets prediction for myostatin-based hypertrophy. These differentially expressed miRNAs are proposed as candidate miRNAs for skeletal muscle hypertrophy.

1. Introduction Skeletal muscle growth and maintenance are essential for animal and human health as 40–60% of the total body mass is composed of skeletal muscles, which provide structural support and enable the body to maintain posture, to control motor movements, and to store energy [1]. Skeletal muscle displays a remarkable plasticity with great capacity to alter its size, structure, and function in response to various stimuli. It therefore plays a vital role in the whole body metabolism. The muscle fibers are established early in life, and muscle growth and development are regulated by an interesting protein named myostatin, a cytokine synthesized in skeletal muscle cells. Myostatin (MSTN), also known as growth and differentiation factor-8 (GDF-8), is a member of the transforming growth factor-𝛽 (TGF-𝛽) superfamily and was identified by McPherron et al. [2]. Myostatin is expressed

in skeletal muscle tissue where it functions to suppress myoblast proliferation and myofiber hypertrophy [3]. Genetic disruption of myostatin causes a dramatic increase in muscle mass. MSTN binds to activin receptor type IIB (ActRIIB) to exert its biological activity [4–6]. The binding of MSTN to its receptor leads to phosphorylation of transcription factors Smad2 and Smad3 along with subsequently forming a complex with Smad4, resulting in nuclear translocation of the Smad complex and consequent regulation of transcription of downstream target genes [7–9]. Myostatin mRNA is conserved across many mammalian species and massive muscle growth is observed in several livestock animal species with mutations in MSTN gene [10]. Previously, studies reported that MSTN-knockout mice show muscle fiber hyperplasia and hypertrophy in comparison with wild type mice [2]. Like other TGF-𝛽 family members, myostatin is synthesized as a precursor protein, which

2 undergoes two posttranslational cleavage events, generating an N-terminal and a C-terminal peptides. The N-terminal peptide is referred to as myostatin propeptide while the Cterminal peptide is the actual mature form of myostatin. Transgenic overexpression of myostatin propeptide cDNA in skeletal muscle increases animal growth and muscle mass [11, 12]. Enhanced muscle mass phenotype in the propeptide transgenic mice primarily results from myofiber hypertrophy rather than myofiber hyperplasia. The size of fast-twitch, glycolytic muscle fiber at 9 weeks of age was increased by 60% compared with wild-type littermates [11]. By systemic comparisons of global mRNA expression in hypertrophic muscle of the transgenic mice using microarray and qRT-PCR techniques, the distinct expression patterns are identified which are comprised of enhanced expressions of myogenic regulatory factors and extracellular matrix components and differentially downregulated expressions of genes related to protein degradation and mitochondrial ATP synthesis [13, 14]. MicroRNAs are reported to act as negative regulators of target gene expression by recruiting silencing complexes to complementary sequence elements in target mRNAs. miRNAs are small 19–24 nucleotide (nt) RNAs that generally modulate gene expression through translational repression or by causing deadenylation and degradation of target mRNAs [15, 16]. Bioinformatic studies have demonstrated that each miRNA has hundreds of target genes in animals, and up to 30% of all animal genes are miRNA targets [17–22]. Previous studies demonstrated that miRNAs play an essential role in skeletal muscle development by regulating gene expression. In skeletal muscle, miRNAs have been implicated in proliferation, differentiation, hypertrophy, regeneration, and disease [23]. They are functionally significant and potentially key regulators of gene expression during skeletal muscle development [24–29]. For example, deletion of a conditional Dicer allele in embryonic skeletal muscle results in perinatal lethality due to skeletal muscle hypoplasia [30]. The pivotal roles of three muscle-specific miRNAs, miR-1, miR133, and miR-206, in the regulation of myogenesis have been well documented [17, 31, 32]. A group of miRNAs, highly enriched in skeletal muscle (referred to as myomiRs), has recently been identified and includes miR-1, miR-133a, miR-133b, miR-206, miR-208, miR-208b, miR-486, and miR499 [33–37]. Several of these miRNAs are organized under bicistronic clusters on the same chromosome (i.e., miR-11/133a-2, miR-1-2/133a-1, and miR-206/133b) and are transcribed together [17, 38, 39]. Regulation of these myomiRs is controlled by key myogenic regulatory factors (MRFs), including myogenic differentiation 1 (MyoD) and myogenin [40, 41] as well as myocyte enhancer factor 2 (MEF2) [42], serum response factor (SRF) [23], and myocardin-related transcription factor-A (MRTF-A) [33]. MyomiRs influence multiple facets of muscle development and function through regulation of the key myogenic genes [23, 40, 43]. Recent studies demonstrated that miR-29b, miR-133a, and 133b regulate myoblast proliferation and differentiation [38, 44], and miR-1 and miR-133 have been reported to regulate different aspects of skeletal muscle development in vitro and in vivo [23]. miR-1 promotes myocyte differentiation by repressing

BioMed Research International the expression of histone deacetylase 4 (HDAC4), a negative regulator of differentiation and a repressor of the MEF2 (myocyte enhancer factor-2) transcription factor [23]. MSTN gene also can regulate certain miRNAs expression through feedback autoregulation mechanism [45]. In Piedmontese cattle with mutated myostatin gene, MSTN gene was found to be targeted by miR-27b and miR-27b expression was correlated with hypertrophic phenotype of Piedmontese cattle [46]. A recent study further confirmed that MSTN regulates miR-27a/b via SMAD3 in feedback autoregulation manner. Increased expression of miR-27a/b was correlated with decreased expression of MSTN and vice versa both in vitro and in vivo mouse model. Loss of SMAD3 was associated with an increased level of MSTN [47]. Although the functional significances of miRNAs in controlling myogenesis have been documented and the majority of miRNAs are abundantly expressed, identifications of novel miRNAs that are expressed at low levels during skeletal muscle development have not been well defined. With robust approaches such as high-throughput deep sequencing technology, we can produce the whole miRNA transcriptomes [48]. In this study, we have attempted to identify candidate miRNAs in hypertrophic skeletal muscle with transgenic overexpressed myostatin propeptide by using transcriptome deep sequencing. Sequencing results have shown that a group of highly abundant known miRNAs were expressed in skeletal muscle. Using computational target prediction software we have identified differentially expressed miRNAs associated with muscle hypertrophy. An interaction network between miRNA and identified targets was constructed.

2. Materials and Method 2.1. Muscle Tissue Sample Collection. Myostatin propeptide transgenic mice were generated by standard microinjection techniques using B6SJL F1 females (Taconic, Germantown, NY) as zygote donors using the transgene MLC-pro construct [11]. The MLC-pro construct consists of myosin light chain (MLC) promoter and enhancer, myostatin cDNA from the 50 untranslated regions to the nucleotides encoding the propeptide cleavage site (RSRR), and SV40 PolyA tail signal sequence. Transgenic mice and wild-type littermate mice were obtained from offspring of MLC-pro transgenic mice mating with B6SJL F1. All procedures and animal care were in accordance with the institution guidelines and approved by the Institutional Animal Care and Use Committee of the University of Hawaii, Hawaii, USA. Myostatin propeptidetransgenic mice were generated by standard microinjection techniques, which has been previously described [9]. Male mice (hemizygous genotype for the transgene) from the high-expressing line were mated with B6SJL F1 wild-type females to produce offspring mice, which were used in this study. Mice were housed in cages; room temperature was maintained at 22∘ C and 12 h light/dark cycle. Mice were weaned at 4 weeks of age and given free access to a chow diet (10% kcal fat, ME3.85 kcal/g). Male mice at 4 months age were sacrificed for muscle tissue dissections and sampling after 8 hr fasting. Gastrocnemius muscle samples

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Table 1: Number of miRNA reads from control and transgenic libraries. Reads Total reads Clean reads Qualified% Mapped reads Mapped%

CN148 11303077 10479962 92.728% 536489 5.119188

CN150 9357529 8375356 89.504% 224550 2.68108

from both legs were immediately dissected from carcass, cleaned from fat, blood and quickly frozen in liquid nitrogen, and later stored at −80∘ C. Two wild-type littermate control samples, that is, CN148 and CN150, and three transgenic mouse samples, that is, TN126, TN135, and TN 329, were, respectively, collected from gastrocnemius muscle. Samples were immediately frozen in liquid nitrogen and stored at −80∘ C freezer. 2.2. Small RNA Library Preparation. Total RNA was isolated using TRIzol reagent (Invitrogen). Approximately 5 𝜇g of total RNA from transgenic and control mice was submitted to the Beijing Genomics Institute (BGI) for Solexa sequencing. In brief, polyacrylamide gel electrophoresis (PAGE) technique was used to fractionate total RNA in the range of 16–30 nt, for sequencing. Fragments were then ligated with proprietary adapters. cDNA was synthesized from total RNA by reverse transcription and amplified to produce libraries for sequencing. 2.3. Analysis of the Output Data. The data as obtained in fastq format was converted to fastq2fast.pl; and revalidated clean miRNA-seq dataset was analyzed using mirDeep software v.2.1.2. The Mus musculus genome Mus musculus. GRCm38.74 was downloaded from Ensembl, and the miRNA reference was obtained from the miRBase database (version 20). The miRNA expression level of each library was normalized by the following formula: Normalized reads count = Reads count × 1, 000, 000 total clean reads count. (1) The Student’s 𝑡-test was used to calculate the 𝑃 value and analyze the expression difference between different groups [59]. The 𝑞 value was calculated by fdrtool in 𝑅 package [60]. 2.4. Novel miRNA Prediction. The known and novel miRNAs were identified from deep sequencing data using miRDeep software v.2.1.2 package [61]. All sequences were mapped to the mouse genome using megaBLAST, and exactly matched sequences were processed for further analysis. Potential precursor miRNA sequences were extracted from the mouse genome and the secondary structures were predicted by RNAfold [62] (Figure 4).

TN126 9734519 9052728 92.996% 177471 1.960415

TN135 10207035 9677365 94.812% 190093 1.964305

TN329 21774919 20070380 92.172% 429933 2.142126856

2.5. Target Prediction. To observe the potential function of miRNAs with significantly differential expression in two groups (transgenic versus wild-type control), we used miRDB (Version 6.2) to predict putative target genes of miRNAs with homologous human miRNAs and target prediction score ≥ 60 [63] (http://mirdb.org/miRDB/index.html). Validated targets module of miRWalk was also used to investigate hosts experimentally verified target genes of differentially expressing miRNAs in the study (http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/) [64]. 2.6. Gene Ontology and Pathway Analysis. Gene ontology analysis was performed by analyzing KEGG pathways using “database for annotation, visualization, and integrated discovery” (DAVID) website [65, 66] with the following parameters: Count = 2 and EASE = 1.0. “Count” means the threshold of minimum gene counts belonging to an annotation term, and “EASE” is a modified Fisher Exact 𝑃 value.

3. Results 3.1. Overview of the Sequencing Data. Total reads from control CN148-11303077 and CN150-9357529 and from MSTN transgenic mice TN126-9734519, TN135-10207035, and TN329-21774919 were obtained. After trimming the adaptor and low quality reads, only clean reads of high quality were used for further analysis, that is, 10479962-CN148, 8375356-CN150, 10130133-TN126, 9052728-TN135, and 9677365TN329 accounting for 92.72%, 89.50%, 93.24%, 92.99%, and 94.81%, respectively, of the original reads. The sequencing data were simplified by grouping all identical sequence reads together; therefore 536489 (CN148), 224550 (CN150), 228238 (TN126), 177471 (TN135), and 190093 (TN329) unique sequences were used for subsequent analysis (Table 1). The most abundant size class in small RNA sequences distribution was 22 nt, followed by 21 and 23 nt (Figure 1), and these estimates were in the accordance with the known 21–23 nt range for miRNAs [67]. To assess the efficiency of deep sequencing for miRNA detections, all sequence reads were annotated and classified by analyzing the sequence tags in relation to the data from miRBase (miRBase V20). The sequence tag annotation demonstrated that known Mus muculus miRNAs accounted for 59% of all sequence reads in the MSTN and control libraries (Figure 2). These results indicate that the deep sequencing data were highly enriched

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BioMed Research International 50000 45000 40000

Read number

35000 30000 25000 20000 15000 10000 5000 0

15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 Length CN148 CN150 TN126

TN135 TN329

Figure 1: miRNA length distribution graph: miRNA length distribution graph depicting that most of all miRNA was consistent length of 22 nt.

69

255 461

Novel miRNAs which are not homologous with other species Novel miRNAs which are homologous with other species Known miRNAs

Figure 2: Annotation of miRNAs showing the number of known, homologous novel, and nonhomologous miRNAs.

for mature miRNA sequences, suggesting that the data are reliable for expression profiling of known miRNAs and deep mining for novel miRNAs. Comparative expression of miRNAs in transgenic and control mice skeletal muscles revealed that 461 miRNAs obtained in the sequencing data matched perfectly with 1908 known Mus musculus miRNAs in the miRBase.

3.2. miRNA Transcriptome Analysis Demonstrated the Presences of Several Highly Abundant miRNAs in Mouse Skeletal Muscle. Almost all known muscle-specific miRNAs (myomiRs) of the more abundant miRNAs were identified in the analyzed muscle samples. The most abundant expressed miRNA was mmu-miR-22a which was represented by approximately 1637847 and 924589 reads in the small RNA libraries of the wild-type mice (CN148 and CN150) and 755381, 1123582, and 3994285 reads in the small RNA libraries of transgenic mice (TN126, TN135, and TN 329), respectively (Table 2, see Additional File 1 in the Supplementary Materials available online at http://dx.doi.org/10.1155/2014/328935). The predominance of miR-133a was consistent with its well established function during skeletal muscle development; miR-378a is also reported to play important role during Mus musculus myogenesis. Two other myomiRs, miR-26a, and miR-27b were high-count sequences in both libraries of transgenic and wild-type mice. MyomiR 208 and 499 were expressed at a very low level in current study. 3.3. Identification of miRNA Isoforms. The miRNAs also represent heterogeneity at 5󸀠 and/3󸀠 end (Figure 3) and these variations from their miRBase reference sequences are referred to as isomiRs [68, 69]. Examples of isomiRs are presented in Figure 3. In one case, the majority isoform, mmumiR-16a-2, the most abundant isoform is identical to the reference in miRBase (Figure 3, Additional File 2). In some other cases, such as mmu-miR-181b-2, mmu-miR-199a-2, and mmu-miR-16a-2, more than one highly abundant isoform was present (Figure 3), indicating that some miRNAs have more than one isoformin specific tissues. 3.4. Novel miRNAs Were Less Abundant and Less Conserved. In addition to profiling known miRNAs, deep sequencing is a powerful strategy for discovering novel miRNAs that may not have been detected by traditionalmethods from sequencing cDNA libraries. We identified a total of 69 putative novel Mus musculus miRNAs from wild-type controls and transgenic mouse sequence tags (Additional File 3). The putative novel miRNAs were less abundant than known miRNAs (Additional File 3). Two novel miRNAs NMmu-14 and NMmu36 having more than 100 read counts were validated and NMmu14 was found to be relatively abundant in comparison with miR NMmu36 (Figure 4) but both of these had read counts greater than 100 in the small RNA library from TN 329 library (Table 3). The novel miRNAs were less evolutionarily conserved (Additional File 3). 3.5. Identifications of Differentially Expressed miRNAs in Hypertrophic Muscle. To identify miRNAs associated with skeletal muscle hypertrophy, we compared the miRNA transcriptoms of skeletal muscle between MSTN propeptide transgenic mice and their littermate control mice. Of the 461 known miRNAs, 57 differentially expressed miRNAs were identified (Additional File 4). The miRNAs, which satisfied the criteria log2FoldChange ≥ 1 or ≤ −1 and 𝑃 ≤ 0.05, 𝑄 value 0.1, were denoted as differentially expressed miRNAs. Cluster

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Table 2: Highly abundant known miRNAs in control and transgenic libraries. Reads miRNA

TN135

Previous studies in skeletal muscle

CN148

CN150

TN126

TN329

mmu-miR-22-3p

1637847

924589

1186390

755381

1123582

mmu-miR-133a-3p

1143874

716877

984332

1004715

1049011

[38]

mmu-miR-486-3p

694107

288434

927009

902799

1325315

[49]

mmu-miR-143-3p

496985

195585

820161

849193

676631

mmu-miR-378a-3p

427775

201482

420654

356711

417747

mmu-miR-10b-5p

326382

192684

699540

645680

909894

mmu-miR-26a-5p

265020

151950

297146

184254

263216

[51]

mmu-miR-27b-3p

190947

85527

204496

151307

174403

[52, 53]

mmu-miR-10a-5p

106751

35960

218660

179584

198961

mmu-miR-126a-5p

104630

34633

173507

98023

148161

mmu-let-7f-5p

76751

57966

85569

62527

66748

mmu-miR-30d-5p

71324

36990

71091

53116

69360

mmu-miR-30e-5p

46295

22850

77046

49869

64934

mmu-miR-191-5p

43014

20310

61773

53825

66552

mmu-miR-30c-5p

37487

18170

36383

25658

36686

mmu-miR-16-5p

28759

13306

33389

30684

29020

[50]

mmu-miR-101a-3p

27883

12254

42753

32687

43902

mmu-let-7i-5p

27804

25012

21811

16454

15724

mmu-miR-100-5p

26629

8764

62824

58392

50326

mmu-miR-1a-1-3p

25824

4872

60020

59300

56369

[55] [56]

mmu-miR-21a-5p

25762

13328

32285

31527

22657

mmu-let-7a-5p

22645

14527

26374

19001

18235

mmu-miR-125a-5p

22035

10736

20767

16909

13863

mmu-miR-127-3p

20888

19550

42560

28964

36344

mmu-miR-125b-5p

18733

10574

17810

18525

12921

mmu-miR-26b-5p

18498

7630

27992

20838

26573

mmu-miR-378d-5p

17270

7758

20914

14457

17438

mmu-miR-133b-3p

17250

7097

41049

66977

46252

mmu-miR-92a-1-3p

16783

12552

11462

14209

10021

mmu-miR-148a-3p

16589

9216

20184

35176

17861

[54]

[57] [57]

[38] [58]

Table 3: Abundantly expressed novel miRNAs in control and transgenic libraries. Name NMmu-14 NMmu-36

Mature sequence ugauuggaagacacucugcaaca gauucggcugaucuggcuggc

CN148 0 457

CN150 0 0

analysis was generated to check the expression of miRNAs in different samples (Figure 5). To validate the differentially expressed miRNAs of the wild-type control and transgenic mice, 8 known miRNAs and 2 novel miRNAs were randomly selected and their expression levels quantified by using real-time RT-PCR (Figures 4 and

Reads TN126 159 0

TN135 129 0

TN329 0 515

Chromosome position

Strand

16 14

— —

6). Of the 8 known miRNAs examined, 7 miRNAs (miR425, miR-26a, miR-1a, miR-199a, miR-101, miR-378, and miR151) showed a consistent pattern with the deep sequencing data (Figures 6(a)–6(j)). These data demonstrated that deep sequencing is a sensitive and reliable method for identifying differentially expressed miRNAs.

6

BioMed Research International mmu-miR-16a-2 󳰀

5 -caugcuuguuccacucuagcagcacguaaauauuggcguagugaaauaaauauuaaacac-3󳰀 exp ....(((((.((((...((.((.((.......)))))))))).)))))........... reads

mm

...............cuagcagcacguaaauauuggcg......................

2

0

................uagcagcacguaaauauug.........................

3

0

................uagcagcacguaaauauugA........................

1

1

................uagcagcacguaaauauugg........................

3

0

................uaCcagcacguaaauauuggc.......................

2

1

................uagcagcacguaaauauuggG.......................

1

1

................uagcagcacguaaaAauuggc.......................

4

1

................uagcagcacguaaauauuggA.......................

7

1

................uUgcagcacguaaauauuggc.......................

1

1

................uaAcagcacguaaauauuggc.......................

2

1

................uagcagcGcguaaauauuggc.......................

1

1

................uagcagcacguaaauauugUc.......................

1

1

................uagGagcacguaaauauuggc.......................

8

1

................uagcagcacguaaauauuggc....................... 2400 ................uagAagcacguaaauauuggc....................... 1

0

................uagcagcacguaaauGuuggc.......................

1

1

................uagcagcaUguaaauauuggc.......................

1

1

1

................uagcagcacguaaauauuggcA...................... 176 ................uagcagcGcguaaauauuggcg...................... 2

1

................uaAcagcacguaaauauuggcg......................

9

1

................uagcagcacguaaaAauuggcg......................

6

1

1

................uagcagcacguaaauauuggcg...................... 25604 ................uUgcagcacguaaauauuggcg...................... 1

1

................uagcagcacguaaauauugCcg......................

1

1

................uagcagcacguaaauauugAcg......................

2

1

................uagcagcacgAaaauauuggcg......................

2

1

................uagcagcacguaaauauuggcC......................

1

1

................uagcagcacguaaauCuuggcg......................

1

1

90

1

87

1

................uagGagcacguaaauauuggcg...................... ................uagcagcacguaaauauuggcgA.....................

0

mmu-miR-133a-1

5󳰀 -gcuaaagcugguaaaauggaaccaaaucgccucuucaauggauuugguccccuucaacca-3󳰀 exp ((....)).(((..((.((.((((((((............)))))))).)).))..))). reads ...aaagcugguaaaauggaacc..................................... 122 ....aagcugguaaaauggaacc..................................... 30

0 seq

....aagcugguaaaauggaacca....................................

16 ....aagcugguaaaauggaaccaa................................... 104 ....aagcugguaaaauggaaccaaa.................................. 37

0 seq

.....agcugguaaaauggaacc..................................... 544 .....agcugguaaaauggaacca.................................... 317

0 seq

.....agcugguaaaauggaaccaa................................... 368 .....agcugguaaaauggaaccaaa.................................. 2409

0 seq

.....agcGgguaaaauggaaccaaa..................................

13

1 seq

.....agcugguaaaauggaaccaaau.................................

40

0 seq

.....agcugguaaaauggaaccaaauc................................ 585 .....agcugguaaaauggaaccaaaucgcc............................. 80 ......gcugguaaaauggaaccaaa..................................

78

......gcugguaaaauggaaccaaauc................................ 106 ......gcugguaaaauggaaccaaaucgcc............................. 13 (a)

Figure 3: Continued.

mm 0 seq 0 seq 0 seq 0 seq 0 seq

0 seq 0 seq 0 seq 0 seq 0 seq

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mmu-miR-188b-2 5󳰀 -uugauggcugcacucaacauucauugcugucgguggguuugaaugucaaccaacucacug-3󳰀 exp .((((((.((((.((((.((((((((....)))))))))))).)).)).)))..)))... reads ...............aacGuucauugcugucgg........................... 7 ...............aacauucaAugcugucgg........................... 6 ...............aaAauucauugcugucgg........................... 6 ...............aacauucauugcugucgg........................... 1742 ...............aacauucauugcugucggG.......................... 2 ...............aacauucauugcugucggu.......................... 50 ...............aacauucauugcugucggA.......................... 38 ...............aacauucauugcugucgguA......................... 30 ...............aacauucauugcugucggug......................... 322 ...............aacauucauugcugucgUugg........................ 1 ...............aacauucauugcugucggugg........................ 107 ...............aacauucauugcugucggugA........................ 422 ...............aacauucauugcugucgguggU....................... 8 ...............aacGuucauugcugucgguggg....................... 31 ...............aacauucauugcugucgguggg....................... 6296 ...............aacauucauugcugucgguggA....................... 51 ...............aaAauucauugcugucgguggg....................... 23 ...............aacauucauugcugucggugggu...................... 612 ...............aacauucauugcugucggugggA...................... 378 ...............aacauucauugcugucgguggUu...................... 4 ...............aacauucauugcuguAggugggu...................... 1 ...............aacauucauugcugucggugggG...................... 92 ...............aacauucauugcugucgguggguu..................... 130 ...............aacauucauugcugucggugggAu..................... 2 ...............aacauucauugcugucgguggguA..................... 36 ...............aacauucUuugcugucgguggguu..................... 1 ...............aacauucauugcugucgguggguuu.................... 49 ................acauucauugcugucgguggg....................... 327 ................acauucauugcugucggugggG...................... 5 ................acauucauugcugucggugggu...................... 30 ................acauucauugcugucggugggA...................... 16

................acauucauugcugucgguggguu.....................

21

mm 1 seq 1 seq 1 seq 0 seq 1 seq 0 seq 1 seq 1 seq 0 seq 1 seq 0 seq 1 seq 1 seq 1 seq 0 seq 1 seq 1 seq 0 seq 1 seq 1 seq 1 seq 1 seq 0 seq 1 seq 1 seq 1 seq 0 seq 0 seq 1 seq 0 seq 1 seq 0 seq

󳰀

5 -mmu-miR-199a-2 uggaagcuucaggagauccugcuccgucgccccaguguucagacuaccuguucaggacaa-3󳰀 exp (((..((....((((......))))...)).))).(((((.(((.....)))..))))). reads ..............................cccGguguucagacuacc............ 1

mm 1 seq

..............................cccaguguucagacuacc............ 62 ..............................cccaguguucagacuaccug.......... 111 ..............................cccGguguucagacuaccug.......... 1

0 seq

0 seq 1 seq

..............................cccaguguucagacuaccugu......... 182 ..............................cUcaguguucagacuaccugu......... 1

0 seq

..............................cccaguguucagacuaccugA.........

4

1 seq

..............................cccaguguucagacuaccuguu........

75

0 seq

1 seq

..............................cccaguguucagacuaccuguA........

11

1 seq

..............................cccUguguucagacuaccuguuc.......

1

1 seq

..............................cccGguguucagacuaccuguuc.......

3

1 seq

..............................cccaguguucagaAuaccuguuc.......

1

1 seq

..............................cccaguguucagacuaccuUuuc.......

2

1 seq

..............................cccaguguucagacuaccuguuA....... 115

1 seq

..............................Accaguguucagacuaccuguuc.......

1 seq

1

..............................cccaguguucagacuaccuguuc....... 863 (b)

Figure 3: Continued.

0 seq

8

BioMed Research International ..............................cccaguguucagaGuaccuguuc.......

1

1 seq

..............................cAcaguguucagacuaccuguuc.......

1

1 seq

..............................cccaguguucUgacuaccuguuc.......

1

1 seq

..............................ccAaguguucagacuaccuguuc.......

3

1 seq

..............................cccaguguucGgacuaccuguuc.......

1

1 seq

..............................cccaguguucagacuaccuguuca......

30

0 seq

..............................ccAaguguucagacuaccuguuca......

1

1 seq

..............................cccaguguucagacuaccuguucG......

1

1 seq

..............................cccaguguucagacuaccuguucaA.....

1

1 seq

5󳰀 -mmu-miR-486

cagccagcucugaucucgcccucccugagggguccuguacugagcugccccgagguccuu-3󳰀 exp ....((((((..((...(((((......)))))...))...)))))).............

reads

mm

............................gUgguccuguacugagcugcccc.........

1

1 seq

...............................Cuccuguacugagcugccccga.......

139

1 seq

...............................guccuguacugagcugccccga.......

13

0 seq

...............................Cuccuguacugagcugccccgag......

614

1 seq

................................uccuguacugagcugccc..........

236

0 seq

................................uccuguacugagcugcccc.........

714

0 seq

................................uccuguacugagcugccccg........

7622

0 seq

................................uccGguacugagcugccccg........

108

1 seq

................................ucAuguacugagcugccccg........

42

1 seq

................................uccuguacugagcugccccgG.......

1139

1 seq

................................uccGguacugagcugccccga.......

723

1 seq

................................ucAuguacugagcugccccga.......

390

1 seq

................................uccuguacugagcugccccga.......

98430

0 seq

................................uccuguacugagcugccccgaC......

974

1 seq

................................uccuguacugagcugccccgGg......

624

1 seq

................................uccuguacugagcugcccUgag......

83

1 seq

................................uccuguacugagcugccccgCg......

25

1 seq

................................uccuguacugagcugAcccgag......

129

1 seq

................................uccuguacugagcugcAccgag......

60

1 seq

................................uccugCacugagcugccccgag......

79

1 seq

................................ucAuguacugagcugccccgag......

2018

1 seq

................................ucGuguacugagcugccccgag......

186

1 seq

................................uccuguacugagcAgccccgag......

124

1 seq

................................uccuguacugaCcugccccgag......

30

1 seq

................................uAcuguacugagcugccccgag......

116

1 seq

................................uccGguacugagcugccccgag......

3561

1 seq

................................uccuguacugagcugccccgaU......

596

1 seq

................................uccuguacugagcugccUcgag......

55

1 seq

................................uccuguacugagcCgccccgag......

79

1 seq

................................uccuguacugagcugccccgaA......

4421

1 seq

................................uccuguacugagcuCccccgag......

150

1 seq

................................uccuguacugagcugccccgag......

507280

0 seq

................................uccuguacugagcugccccgagA.....

53390

1 seq

................................uccuguacugagcugccccgagg.....

2228

0 seq

................................uccuguacugagcugccccgagU.....

8278

1 seq

................................uccuguacugagcugccccgaggu....

191

0 seq

................................uccuguacugagcugccccgagAu....

556

1 seq

................................uccuguacugagcugccccgaggA....

73

1 seq

(c)

Figure 3: Continued.

BioMed Research International

9

................................uccuguacugagcugccccgagUu....

4893

1 seq

................................uccuguacugagcugccccgagguA...

129

1 seq

................................uccuguacugagcugccccgagguU...

128

1 seq

..................................cuguacugagcugccccgag......

374

0 seq

..................................cuguacugagcugccccgagg.....

1040

0 seq

..................................cuguacugagcugccccgaggu....

194

0 seq

..................................cuguacugagcugccccgagguU...

163

1 seq

..................................cuguacugagcugccccgagguA...

274

1 seq

..............uucaagAaauucaggauaggu.........................

8

1 seq

..............uucUaguaauucaggauaggu.........................

2

1 seq

..............uucaaguaauuUaggauaggu.........................

8

1 seq

..............uucaaUuaauucaggauaggu.........................

3

1 seq

..............uuUaaguaauucaggauaggu.........................

2

1 seq

..............uucaaguaauucaAgauaggu.........................

5

1 seq

..............uucaaguaauucaggauaggC.........................

269

1 seq

..............uucaaguaGuucaggauaggu.........................

2

1 seq

..............uucaaguaauucaggaCaggu.........................

2

1 seq

..............uucaagCaauucaggauaggu.........................

5

1 seq

..............Gucaaguaauucaggauaggu.........................

2

1 seq

..............uucaaguaauucaggauaggu.........................

11195

0 seq

..............Aucaaguaauucaggauaggu.........................

3

1 seq

..............uucaGguaauucaggauaggu.........................

3

1 seq

..............uucaaguaauucaggauaggG.........................

13

1 seq

..............uucaaguaauuAaggauaggu.........................

2

1 seq

..............uucaaAuaauucaggauaggu.........................

2

1 seq

..............uuGaaguaauucaggauaggu.........................

6

1 seq

..............uucaagGaauucaggauaggu.........................

4

1 seq

..............uuAaaguaauucaggauaggu.........................

42

1 seq

..............uucaaguaauCcaggauaggu.........................

57

1 seq

..............uucaaguaaCucaggauaggu.........................

1

1 seq

..............uucaaguaauucaggauaAgu.........................

2

1 seq

..............uucGaguaauucaggauaggu.........................

46

1 seq

..............uuAaaguaauucaggauagguu........................

7

1 seq

..............uucaaguaauCcaggauagguu........................

8

1 seq

..............uucaaguaauucaggauaggCu........................

12

1 seq

..............uucaaguaauucaggauagguA........................

528

1 seq

..............uucaaguaauucaggauagguC........................

3

1 seq

..............uucaaguaauucaggauagguu........................

917

0 seq

..............uGcaaguaauucaggauagguu........................

1

1 seq

..............uucaaguaauucaggauagguug.......................

23

0 seq

..............uucaaguaauucaggauagguuC.......................

29

1 seq

..............uucaaguaauucaggauagguuU.......................

29

1 seq

..............uucaaguaauucaggauagguAg.......................

8

1 seq

..............uucaaguaauucaggauagguuA.......................

929

1 seq

..............uucaaguaauucaggauagguugA......................

3

1 seq

...............ucaaguaauucaggauaggu.........................

3

0 seq

...............ucaaguaauucaggauaggC.........................

2

1 seq

mmu-miR-26b 5󳰀 -ugcccgggacccaguucaaguaauucaggauagguuguggugcugaccagccuguucucc

mm

(d)

Figure 3: IsomiRs from different miRNAs: reads alignments of the various isoforms of several mmu-miRs are presented. The sequence of the mmu-miR hairpin is presented in the top line; the brackets below denote the secondary structure. Reads aligned with the mature mmu-miR sequence as reported in miRBase are denoted by a series of asterisks. The number of reads corresponding to each sequence is presented on the right.

10

BioMed Research International 5󳰀

3

U

A

󳰀

A C

A

C

C

A

C

A

C

G

U

C

G

U

C

U

C

C

A

A

AC

C C

A

U

A G

A

U

C

A

G

C G

U

A

U

U U

C

A

G

G

U

C

C

A

G

U

U G

U

G

U

C

G

A

U C

U

NMmu-14

C

G

G

(a) 5󳰀

C

A

U U

3󳰀

G

A

U

C

A

C

U

A

G

U

C G

C

C

U

G U

C

G

G

C

C

G

G

G

U

U

G

G

G

C

U

U

A

G

C

A U U

NMmu-36

Mature Loop Star

(b)

Figure 4: Predicted secondary structure of novel miRNAs: novel miRNAs secondary structure was predicted using miRDeep Software, where red color indicates the mature sequence, yellow color indicates loop sequence, blue color indicates the predicted star sequence, and purple color indicates miRNA star sequence.

3.6. Target Prediction for Differentially Expressed miRNAs. To ascertain the query how miRNAs function in concert with their target genes in muscle tissue, we identified targets of the differentially expressed miRNAs. Of the 57 differentially expressed miRNAs, 20 abundant miRNAs were selected to predict the targets and 4,413 annotated mRNA transcripts were identified (Additional File 5). 3.7. Gene Ontology and Pathway Analysis. Predicted 4,413 targets were analyzed for pathway analysis and the results showed that approximately 110 pathways are involved in corresponding miRNAs. Out of 110 pathways, 16 pathways are found to be associated with the most number of targeted genes (Figure 7), including mTOR signaling, insulinlike growth factor, MAPK signaling, and TGF-𝛽 signaling. We also found that 20 differentially expressed miRNAs target many important genes such as FST, SMAD3, SMAD4, TGFBR1, ACRVR1a, ACVR1C, ACVR2a, ACVR, AKT3, and MEF2C (Table 4), which have been known to play a vital role in MSTN signalling pathway. 3.8. Results of Q-PCR Validation. Out of 57 differentially expressed miRNAs, 8 known miRNAs and 2 novel miRNAs were confirmed by using real-time RT-PCR (Figures 6(a)– 6(j)). Out of 8 known miRNAs, 6 miRNAs have been functionally linked to myogenesis (i.e., miR-1a, miR-26a, miR-133a and miR-199a, miR-101, and miR-378 [38, 50, 51, 54, 55, 70]). The levels of these miRNA expressions from Q-PCR analysis

are consistent with the RNA sequence data in both wild-type and transgenic mice.

4. Discussion 4.1. miRNA Identifications from Myostatin Propeptide Transgenic and Wild-Type Mice. Direct sequencing of RNA molecules with next-generation sequencing (NGS) technology has revolutionized the analysis of transcriptome due to highthroughput scale and low cost. Recent development with Illumina’s microRNA-Seq by Genome Analyzer System had enabled direct identification and profiling of microRNAs in various organisms [71]. The present study identified a total of 461 miRNAs, of which 57 were known and differentially expressed and 69 were novel in skeletal muscle tissue. Of the 57 differentially expressed miRNAs mmu-mir-133a was the most abundant in the wild-type control (32, 12,465.607) and transgenic (57, 10,658.17) mice. It is previously reported that miR-133a enhances myoblast proliferation by repressing serum response factor (SRF) [23]. miR-206 was demonstrated to be the most abundant miRNA in skeletal muscles of broilers (131,609 reads) and layers (222,998 reads) [48]. Considering these previous studies, we presume that miR-133a and miR-206 play an important role in skeletal muscle development. In our study, we observed that miR-206 expression level was significantly lower than miR-133a in comparison to previous studies. miR-206 is reported to be one of the skeletal muscle-specific myomiR and many studies have documented its pivotal role in skeletal muscle differentiation

BioMed Research International

11

Count

Color key and histogram 50 20 0 −1

0

1

Row Z-score mmu-miR-133a-3p mmu-miR-486-3p mmu-miR-143-3p mmu-miR-10b-5p mmu-miR-22-3p mmu-miR-26a-5p mmu-miR-27b-3p mmu-miR-378a-3p mmu-miR-30e-5p mmu-miR-191-5p mmu-miR-1a-1-3p mmu-miR-133b-3p mmu-miR-16-5p mmu-miR-21a-5p mmu-let-7a-5p mmu-miR-26b-5p mmu-miR-101a-3p mmu-miR-30c-5p mmu-miR-125a-5p mmu-miR-125b-5p mmu-miR-92a-1-3p mmu-miR-378d-5p mmu-miR-29a-3p mmu-miR-151-5p mmu-let-7g-5p mmu-miR-381-3p mmu-miR-136-3p mmu-miR-98-5p mmu-miR-128-3p mmu-miR-148b-3p mmu-miR-340-5p mmu-miR-93-5p mmu-miR-7068-3p mmu-miR-361-5p mmu-miR-410-3p mmu-miR-301a-3p mmu-miR-19b-1-3p mmu-miR-425-5p mmu-miR-193b-3p mmu-miR-365-3p mmu-miR-107-3p mmu-miR-15a-5p mmu-miR-196b-5p mmu-miR-29b-1-3p mmu-miR-300-3p mmu-miR-872-5p mmu-miR-3068 mmu-miR-341-3p mmu-miR-17-5p mmu-miR-155-5p mmu-miR-193a-3p mmu-miR-1249-3p mmu-miR-409-5p mmu-miR-190a-3p mmu-miR-582-3p mmu-miR-653-5p mmu-miR-7a-3p mmu-miR-374b-5p mmu-miR-1198-5p mmu-miR-6516-5p mmu-miR-382-5p mmu-miR-1933-3p mmu-miR-493-5p mmu-miR-1968-5p mmu-miR-1947-5p mmu-miR-1191b-5p mmu-miR-7015-3p mmu-miR-7236-3p mmu-miR-592-5p mmu-miR-196a-5p mmu-miR-8103-5p mmu-miR-187-3p mmu-miR-666-5p mmu-miR-3089-3p mmu-miR-449a-5p mmu-miR-3060-3p mmu-miR-296-5p mmu-miR-6715-5p mmu-miR-1193-3p mmu-miR-3072-3p mmu-miR-669b-5p mmu-miR-3475-3p mmu-miR-470-5p mmu-miR-6418-3p mmu-miR-873a-5p mmu-miR-6929-3p mmu-miR-383-5p mmu-miR-543-3p mmu-miR-153-3p mmu-miR-7224-3p mmu-miR-708-3p mmu-miR-211-5p mmu-miR-122-5p mmu-miR-25-3p mmu-miR-103-5p mmu-miR-29c-3p mmu-miR-30b-5p mmu-miR-24-1-3p mmu-let-7f-5p mmu-miR-30d-5p

CN148

CN150

TN329

TN126

TN135

Figure 5: Identification of differentially expressed miRNAs in control and transgenic mice: Cluster analysis of differentially expressed miRNAs in control and transgenic mice based on the read counts obtained from deep sequencing data. CN: Control, TN: Transgenic.

[43, 49, 72–75] and miR-133a in regulating myogenesis by increasing muscle cell proliferation [23, 38, 76]. Differences in the expression levels of both the miRNAs (miR-133a and miR-206) in skeletal muscle libraries could reflect different roles of these miRNAs in terms of myogenesis regulation [77]. Similar to miR-133a and miR206, miR-1 also regulate muscle differentiation and development [23, 77–79]. miR1 and miR-133 modulate skeletal-muscle-cell proliferation and differentiation by repressing the activity of HDAC4 (histone deacetylase 4; a signal-dependent inhibitor of muscle

differentiation) and SRF, respectively, thereby establishing negative-feedback loops for muscle-cell differentiation [23]. In addition to well-known myomiRs, recent studies have demonstrated that miR-486 [49], miR-378 [50], miR181a [80], miR-21a, miR-101a, and miR-151 [54] are also involved in regulation of myogenesis and several other ubiquitously expressed miRNAs have also been found to participate in myogenesis, including miR-26a [51], miR-27b [52, 53], and miR-29 [44]. All these nine miRNAs were also found to be abundantly expressed in our sequence libraries, which

12

BioMed Research International Mmu-miR-101a-3p 2000

250 200 150 100 50

1500 1000 Relative expression

Relative expression

Mmu-miR-26a

0.08 0.06 0.04

500

0.8 0.6 0.4 0.2

0.02

0.0

0.00 TN126

TN135

TN329 CN148 Sample name

TN126

CN150

TN329

CN148

CN150

CN148

CN150

TN329 CN148 Sample name

CN150

Sample name B

B (a)

(b)

Mmu-miR-133a-3p

Mmu-miR-151-5p

3500 3000 2500 2000 1500 1000 500

2.5

Relative expression

Relative expression

TN135

0.8 0.6 0.4

2.0 1.5 1.0 0.5

0.2 0.0

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Sample name

B

B (c)

(d)

400 350 300 250 200 150 100 50 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

14 12 Relative expression

Relative expression

Mmu-miR-378a-3p Mmu-miR-199a-3p

10 8 6 4 2 0

TN126

TN135

TN329

CN148

CN150

TN126

Sample name B

TN135

B (e)

(f)

Figure 6: Continued.

BioMed Research International

13 Mmu-miR-1a-1-3p

Mmu-miR-425-5p

Relative expression

Relative expression

10 8 6 4

3500 3000 2500 2000 1500 1000 500 0.8 0.6 0.4

2

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(h)

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Relative expression

CN148 TN329 Sample name

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B (i)

(j)

Figure 6: Validation of differentially expressed known and novel miRNAs: (a)–(j) eight differentially expressed and two novel miRNAs were selected for sequencing results validation.

indicated their important roles in the hypertrophic skeletal muscle of the myostatin propeptide transgenic mice. 4.2. Differentially Expressed miRNA Responsible for Skeletal Muscle Hypertrophy. A total of 57 differentially expressed miRNAs were identified; out of these miR-133a, miR-378a, and miR-26 were highly abundant. MiR-133a [23, 38], miR378a [50], miR-26a [51], miR-27b [52, 53], miR-21a [56], miR-29a [44], miR-148 [58], and miR-103 are skeletal muscle specific miRNAs and play a vital role in muscle differentiation and proliferation as reported in previous studies. miR-181 is upregulated during myocyte differentiation and represses homeobox protein Hox-A11, a repressor of muscle-cell differentiation, thereby allowing new muscle growth [81]. The expression of miR-133 (miR-133a, miR-133b), miR-1, and miR181 (miR-181a, miR-181b, and miR-181c) was profiled in muscle

from patients affected by myotonic dystrophy type1 and it was observed that they were specifically induced during myogenesis [82]. miR-148a promotes myogenic differentiation and downregulates ROCK1 gene at the translational level and plays a positive role in skeletal muscle development through the RhoA/ROCK pathway [58]. MiR-1 and miR-133a were proposed to contribute in muscle hypertrophy by the removal of their transcriptional inhibitory effect on growth factors such as IGF-1. Likewise, a regulatory feedback loop was demonstrated in vitro where IGF-1 downregulated miR-1 via the Akt/FoxO3a pathway [55]. It was shown that FoxO3a increased the levels of miR1 resulting in reduced IGF-1 protein levels. The results from this study suggest that a number of differentially expressed miRNAs that could exert novel functions in skeletal muscle development. We identified targets for 20 highly abundant

BioMed Research International

Pathways

14

Cell cycle Pathways in cancer Axon guidance Cytokine-cytokine receptor interaction Focal adhesion Tight junction PPAR signaling pathway Ubiquitin mediated proteolysis Melanoma mTOR signaling pathway Melanogenesis TGF-beta signaling pathway Regulation of actin cytoskeleton MAPK signaling pathway Wnt signaling pathway Insulin signaling pathway 0

20

40

60 80 100 Genes count

120

140

Figure 7: Pathways with respective gene numbers: top 16 Pathways predicted to be targeted differentially expressed miRNAs given on 𝑌-axis and genes count on 𝑋-axis.

Table 4: Abundant differentially expressed miRNAs their targets and predicted pathways for targets. miRNAs

MAPK signaling pathway EGFR, RPS6KA5, KRAS, SOS1, MAP2K4, MKNK2, CACNG2, FGF1, RAPGEF2, CACNA1A, ACVR1C, PRKCB CACNA2D1, FGF9, NF1, GNA12, RAF1, MKNK1, MAPK8, AKT3

TGF-𝛽 signaling pathway

mTOR signaling Pathway

SMAD9, IFNG, RPS6KB1, SMAD1, ACVR1C

PDPK1, RPS6KB1, RICTOR

1

miR-27b

2

miR-16a

3

miR-21a

4

miR-23a

5

miR-26a

RPS6KA6, RAP1A

RPS6KA6, ULK1, ULK2

6

miR-29b

DUSP2, GNG12

VEGFA, IGF1, EIF4E2

7

miR-30b

TAOK1, RASA1, MAP3K12

PIK3CD, PRKAA2

8

miR-30c

TAOK1, RASA1, MAP3K12

PIK3CD, PRKAA2

9

miR-30d

TAOK1, RASA1, MAP3K12

PIK3CD, PRKAA2

10

miR-30e

TAOK1, RASA1, MAP3K12

PIK3CD, PRKAA2

11

miR-101a

DUSP1, NLK, TGFBR1, CACNB2, FGF10

TGFBR1, SMAD3

HIF1A, PRKAA1

12

miR-103

PRKCA, CDC25B

BMP2

CAB39L, PGF

13

miR-125a

TRAF6, MAP3K11

INHBE

14

miR-133a

ARRB1, B230120H23RIK, FGF12, MECOM

PPP2CA, PPP2CB

15

miR-148b

SOS2, IKBKB, GADD45A

INHBB, NOG, ACVR1

ULK3, RICTOR, MLST8

16

miR-199

ACVR2A, RBL1, ACVR1C

MTOR

17

miR-378a

MAP3K4, ACVR1C CACNG8, RASGRF1, ELK4, PLA2G12A, TRAF6

18

miR-425a

MEF2C, DUSP2, MAP2K1, TGFBR2, IL1A

TGFBR2, FST

IGF1, CAB39, FIGF

19

miR-582

SMAD1, TGFB2

20

miR-3068

BDNF, PPM1B, RASA2, TGFB2 RASGRP3, MAPT, FGF12, CACNA1C, IL1A

MAP3K7, NTF3, FASL, MAPK10, DUSP8 PTPN7, MAP4K4, MAP3K5, PAK2, RRAS2, CACNA1E, FAS, STK4, CHUK

PIK3R1, AKT3 PITX2 SMAD5, SMURF2

PPP2CB, ID4, ACVR1

PIK3CB, PIK3R3

PIK3CA

BioMed Research International

15

miR-148b

miR-23a

miR-103a NOG

SMURF2 IFNG

miR-27b

miR-425

INHBB

SMAD5 SMAD9

BMP2

TGFBR1

FST

ACVR1

TGFBR2

RPS6KB1 ACVR1C

miR-3068

miR-133a

miR-101a

ID4 SMAD3

miR-582

PPP2CB

SMAD1 ACVR2a TGFB2

PPP2CA

miR-125a

miR-21a

miR-199a

INHBE

PITX2

RBL1 (a)

TGF𝛽

miR-425

Follistatin

Myostatin

miR-199a

Receptor complex

miR-101a ActRIIB

TGFBR1

P

P SMAD2

SMAD3

SMAD2

SMAD3

SMAD4

SMAD4

miR-101a

Muscle hypertrophy Muscle differentiation (b)

Figure 8: (a) Interaction network of differentially expressed miRNAs and their targets: network construction can be divided into two components: miRNA-target and target-target interaction, and candidate miRNA targets were predicted by mRDB. Target-target pairs interaction were searched from STRING database. In the network yellow nodes represented miRNAs and pink nodes represented targets, red lines denote miRNA-target interaction, and blue line denotes target-target interaction. These differentially expressed miRNAs predicted to be involved in TGFbeta signaling pathway. (b) MSTN signaling in skeletal muscle and miRNAs involved in the pathway: TGFb legend binds to the activin receptor two beta which interact with the TGFBR1 and they form a receptor complex, and mature myostatin inhibited by follistatin binds to the receptor complex which in turn phosphorylates regulatory SMADs, such as SMAD2 or 3. Phosphorylated regulatory SMADs are recognized by and associated with mediator co-SMADs, such as SMAD4, and translocated into the nucleus to interact with target gene promoter cis-elements and to regulate myostatin-specific gene expression. miRNAs were predicted to regulate the genes in TGFb signaling.

16 differentially expressed miRNAs and predicted genes were analyzed to search the pathways. Sixteen pathways were selected including mTOR signaling, insulin-like growth factor, MAPK signaling and TGF-𝛽 signaling and other pathways such as ubiquitin mediated proteolysis, focal adhesion, Wnt signaling pathway, regulation of actin cytoskeleton, cytokine-cytokine receptor interaction, tight junction, and cell cycle which are known to be involved in cell and tissue structure. Of these, we particularly focused on TGF-𝛽 signaling, as many studies reported that TGF-𝛽 family plays important roles in regulating muscle growth and development [83, 84]. Pathways analysis predicted that miR-101a is targeting TGFBR1 and SMAD3, miR-582 is targeting TGFB2, SMAD1, miR-425 is targeting TGFBR2 and FST, miR-199a is targeting ACVR2a, miR-148 is targeting ACVR1, and miR-103 is targeting BMP2 in TGF-𝛽 signaling pathway. A network was constructed to show the miRNA-target and target-target interaction in TGF-𝛽 signaling (Figure 8(a)). Myostatin (MSTN) is predominantly expressed in skeletal muscle and plays a crucial role in muscle development and also signals through the TGF-𝛽 branch [85] (Figures 8(a) and 8(b)). Myostatin binds to the TGFBR1 and makes a receptor complex for binding with ActRIIB, and activation of this receptor complex leads to the phosphorylation of SMAD2 and SMAD3.

5. Conclusion This study was carried out to identify differentially expressed miRNA transcriptome in myostatin propeptide transgenic mice and to evaluate the miRNAs associated with hypertrophic skeletal muscle. Predicted target and pathway analysis concluded that miR-101a is targeting TGFBR1 and SMAD3, miR-425 is targeting TGFBR2 and FST, miR-582 is targeting SMAD1 and TGFB2, miR-148 is targeting ACVR1, and miR-199a is targeting AcvR1a gene in the TGF-𝛽 signaling pathway. As far as we have known, these miRNAs and their gene targets, which are crucial to myostatin signaling pathway, have not been reported earlier. Annotations of these major miRNAs to the myostatin signal pathway in relation to skeletal muscle hypertrophy can provide important guidance for the application of miRNA approaches to enhancing skeletal muscle growth and development.

Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments The authors thank Dr. Mukesh (WII, Dehradun) for reviewing the paper and thank Dr. Wei Wei (HAU, Wuhan) for her timely help and support. Thanks are due to Yuanxin Miao for assistances in RNA extraction. This work was supported by the Chinese National Major Special Project on New Varieties Cultivation for Transgenic Organisms (2013ZX08006005), the Fundamental Research Funds for the Central

BioMed Research International Universities of China, National Outstanding Youth Foundation of the National Natural Science Foundation of China (31025026), and Alan M Krassner and Ingeborg v. F. McKee Fund of the Hawaii Community Foundation (ID no. 13P60310).

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